test

library(bslib)

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library(tidyverse)

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library(plotly)

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library(tmap)
library(sf)

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library(trelliscopejs)
library(shinythemes)
library(ggplot2)
library(gapminder)
library(lubridate)

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library(reshape)

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library(ggstatsplot)

## You can cite this package as:
##      Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
##      Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167

library(RColorBrewer)
library(sftime)
library(ggthemes)
library(FunnelPlotR)
library(pheatmap)

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library(ggplotify)

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library(heatmaply)

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## 
## ======================
## Welcome to heatmaply version 1.3.0
## 
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
## 
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## You may ask questions at stackoverflow, use the r and heatmaply tags: 
##   https://stackoverflow.com/questions/tagged/heatmaply
## ======================

participantData <- read_csv("data/facet/Participants.csv")

## Rows: 1011 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): educationLevel, interestGroup
## dbl (4): participantId, householdSize, age, joviality
## lgl (1): haveKids
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

incomeExpenseBalanceParticipant <- read_csv("data/participant/comprehensiveParticipantInfoFinal.csv")

## Rows: 283812 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): timestamp
## dbl (9): participantId, income, allExpense, balance, foodExpense, educationa...
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

incomeExpenseBalanceParticipant$timestamp <- as.Date(incomeExpenseBalanceParticipant$timestamp, format =  "%d/%m/%Y")
incomeExpenseBalanceTotal <- read_csv("data/overall/total.csv")

## Rows: 450 Columns: 9
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## Delimiter: ","
## chr (1): timestamp
## dbl (8): income, allExpense, balance, foodExpense, rentAdjustmentExpense, ed...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

incomeExpenseBalanceMin <- read_csv("data/overall/min.csv")

## Rows: 450 Columns: 9
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## Delimiter: ","
## chr (1): timestamp
## dbl (8): income, allExpense, balance, educationalExpense, rentAdjustmentExpe...
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

incomeExpenseBalanceAverage <- read_csv("data/overall/average.csv")

## Rows: 450 Columns: 9
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incomeExpenseBalanceMax <- read_csv("data/overall/max.csv")

## Rows: 450 Columns: 9
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## Delimiter: ","
## chr (1): timestamp
## dbl (8): income, allExpense, balance, educationalExpense, rentAdjustmentExpe...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

incomeExpenseBalanceTotal$timestamp <- as.Date(incomeExpenseBalanceTotal$timestamp, format =  "%d/%m/%Y")
incomeExpenseBalanceMin$timestamp <- as.Date(incomeExpenseBalanceMin$timestamp, format =  "%d/%m/%Y")
incomeExpenseBalanceAverage$timestamp <- as.Date(incomeExpenseBalanceAverage$timestamp, format =  "%d/%m/%Y")
incomeExpenseBalanceMax$timestamp <- as.Date(incomeExpenseBalanceMax$timestamp, format =  "%d/%m/%Y")
use_this_for_balance <- read_csv("data/heatmap/balance_heatmap_test.csv")

## Rows: 19373 Columns: 10
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## Delimiter: ","
## chr (1): timestamp
## dbl (9): participantId, income, allExpense, balance, foodExpense, educationa...
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

use_this_for_balance$timestamp <- as.Date(use_this_for_balance$timestamp, format =  "%d/%m/%Y")

df_total <- use_this_for_balance    
df_use_this <- subset(df_total, select = c(participantId, timestamp, balance))

balance_matrix <- acast(df_use_this, timestamp~participantId, value.var="balance")

## Aggregation function missing: defaulting to length

heatmaply(balance_matrix)